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llama : add SEA-LION support (#6448)
* initial commit for sealion support * add sealion support * minor fix * q/k ln and pos_embd only if required * Apply suggestions from code review Co-authored-by: Georgi Gerganov <ggerganov@gmail.com> * minor : clear whitespaces --------- Co-authored-by: bryan <bryansiow@aisingapore.org> Co-authored-by: Georgi Gerganov <ggerganov@gmail.com>
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@ -118,6 +118,7 @@ Typically finetunes of the base models below are supported as well.
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- [x] [Mamba](https://github.com/state-spaces/mamba)
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- [x] [Xverse](https://huggingface.co/models?search=xverse)
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- [x] [Command-R](https://huggingface.co/CohereForAI/c4ai-command-r-v01)
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- [x] [SEA-LION](https://huggingface.co/models?search=sea-lion)
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**Multimodal models:**
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@ -510,6 +510,16 @@ class BloomModel(Model):
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class MPTModel(Model):
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model_arch = gguf.MODEL_ARCH.MPT
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def set_vocab(self):
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try:
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self._set_vocab_gpt2()
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except:
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self._set_vocab_sentencepiece()
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self.gguf_writer.add_add_bos_token(False)
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self.gguf_writer.add_pad_token_id(3)
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self.gguf_writer.add_eos_token_id(1)
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self.gguf_writer.add_unk_token_id(0)
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def set_gguf_parameters(self):
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block_count = self.hparams["n_layers"]
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self.gguf_writer.add_name(self.dir_model.name)
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@ -523,7 +533,10 @@ class MPTModel(Model):
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self.gguf_writer.add_layer_norm_eps(1e-5)
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if self.hparams["attn_config"]["clip_qkv"] is not None:
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self.gguf_writer.add_clamp_kqv(self.hparams["attn_config"]["clip_qkv"])
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if self.hparams["attn_config"]["alibi"]:
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self.gguf_writer.add_max_alibi_bias(self.hparams["attn_config"]["alibi_bias_max"])
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else:
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self.gguf_writer.add_max_alibi_bias(0.0)
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def write_tensors(self):
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block_count = self.hparams.get("n_layers", self.hparams.get("num_hidden_layers"))
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@ -367,6 +367,9 @@ MODEL_TENSORS: dict[MODEL_ARCH, list[MODEL_TENSOR]] = {
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MODEL_TENSOR.FFN_DOWN,
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MODEL_TENSOR.FFN_UP,
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MODEL_TENSOR.FFN_ACT,
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MODEL_TENSOR.ATTN_Q_NORM,
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MODEL_TENSOR.ATTN_K_NORM,
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MODEL_TENSOR.POS_EMBD,
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],
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MODEL_ARCH.GPTJ: [
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MODEL_TENSOR.TOKEN_EMBD,
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@ -285,11 +285,13 @@ class TensorNameMap:
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MODEL_TENSOR.ATTN_Q_NORM: (
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"language_model.encoder.layers.{bid}.self_attention.q_layernorm",
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"model.layers.{bid}.self_attn.q_layernorm", # persimmon
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"transformer.blocks.{bid}.attn.q_ln", # sea-lion
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),
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MODEL_TENSOR.ATTN_K_NORM: (
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"language_model.encoder.layers.{bid}.self_attention.k_layernorm",
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"model.layers.{bid}.self_attn.k_layernorm", # persimmon
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"transformer.blocks.{bid}.attn.k_ln", # sea-lion
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),
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MODEL_TENSOR.ROPE_FREQS: (
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42
llama.cpp
42
llama.cpp
@ -594,6 +594,9 @@ static const std::map<llm_arch, std::map<llm_tensor, std::string>> LLM_TENSOR_NA
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{ LLM_TENSOR_FFN_DOWN, "blk.%d.ffn_down" },
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{ LLM_TENSOR_FFN_UP, "blk.%d.ffn_up" },
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{ LLM_TENSOR_FFN_ACT, "blk.%d.ffn.act" },
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{ LLM_TENSOR_POS_EMBD, "position_embd" },
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{ LLM_TENSOR_ATTN_Q_NORM, "blk.%d.attn_q_norm"},
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{ LLM_TENSOR_ATTN_K_NORM, "blk.%d.attn_k_norm"},
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},
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},
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{
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@ -4867,6 +4870,7 @@ static bool llm_load_tensors(
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case LLM_ARCH_MPT:
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{
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model.tok_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_TOKEN_EMBD, "weight"), {n_embd, n_vocab});
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model.pos_embd = ml.create_tensor(ctx_input, tn(LLM_TENSOR_POS_EMBD, "weight"), {n_embd, hparams.n_ctx_train}, false);
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// output
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{
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@ -4905,6 +4909,12 @@ static bool llm_load_tensors(
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layer.ffn_up = ml.create_tensor(ctx_split, tn(LLM_TENSOR_FFN_UP, "weight", i), {n_embd, n_ff});
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layer.ffn_up_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_UP, "bias", i), {n_ff}, false);
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layer.attn_q_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "weight", i), {n_embd}, false);
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layer.attn_q_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_Q_NORM, "bias", i), {n_embd}, false);
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layer.attn_k_norm = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "weight", i), {n_embd}, false);
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layer.attn_k_norm_b = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_ATTN_K_NORM, "bias", i), {n_embd}, false);
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// AWQ ScaleActivation layer
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layer.ffn_act = ml.create_tensor(ctx_layer, tn(LLM_TENSOR_FFN_ACT, "scales", i), {n_ff}, false);
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}
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@ -7721,6 +7731,7 @@ struct llm_build_context {
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GGML_ASSERT(n_embd_head == hparams.n_embd_head_k);
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struct ggml_tensor * cur;
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struct ggml_tensor * pos;
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struct ggml_tensor * inpL;
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inpL = llm_build_inp_embd(ctx0, lctx, hparams, batch, model.tok_embd, cb);
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@ -7731,6 +7742,16 @@ struct llm_build_context {
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// positions of the tokens in the KV cache
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struct ggml_tensor * KQ_pos = build_inp_KQ_pos();
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if (model.pos_embd) {
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// inp_pos - contains the positions
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struct ggml_tensor * inp_pos = build_inp_pos();
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pos = ggml_get_rows(ctx0, model.pos_embd, inp_pos);
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cb(pos, "pos_embd", -1);
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inpL = ggml_add(ctx0, inpL, pos);
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cb(inpL, "inpL", -1);
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}
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for (int il = 0; il < n_layer; ++il) {
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struct ggml_tensor * attn_norm;
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@ -7765,12 +7786,33 @@ struct llm_build_context {
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cb(Kcur, "Kcur", il);
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cb(Vcur, "Vcur", il);
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// Q/K Layernorm
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if (model.layers[il].attn_q_norm) {
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Qcur = llm_build_norm(ctx0, Qcur, hparams,
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model.layers[il].attn_q_norm,
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model.layers[il].attn_q_norm_b,
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LLM_NORM, cb, il);
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cb(Qcur, "Qcur", il);
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Kcur = llm_build_norm(ctx0, Kcur, hparams,
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model.layers[il].attn_k_norm,
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model.layers[il].attn_k_norm_b,
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LLM_NORM, cb, il);
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cb(Kcur, "Kcur", il);
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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Kcur = ggml_reshape_3d(ctx0, Kcur, n_embd_head, n_head_kv, n_tokens);
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cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
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model.layers[il].wo, model.layers[il].bo,
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Kcur, Vcur, Qcur, KQ_mask, nullptr, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
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} else {
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Qcur = ggml_reshape_3d(ctx0, Qcur, n_embd_head, n_head, n_tokens);
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cur = llm_build_kv(ctx0, model, hparams, kv_self, gf,
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model.layers[il].wo, model.layers[il].bo,
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Kcur, Vcur, Qcur, KQ_mask, KQ_pos, n_ctx, n_tokens, kv_head, n_kv, 1.0f/sqrtf(float(n_embd_head)), cb, il);
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}
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}
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if (il == n_layer - 1) {
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// skip computing output for unused tokens
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